The present invention relates to video quality of service, and more particularly to the measurement of blurring in video sequences due to video processing.
A number of different factors influence the visual quality of a digital video sequence. Numerous subjective studies have shown that the amount of blurring in the video sequence is one of the factors that has the strongest influence on overall visual quality. Therefore the ability to objectively measure the amount of blurring in the video sequence is a key element in a video quality metric. A codec developer or compression service provider may use this information to modify the compression process and make informed decisions regarding the various tradeoffs needed to deliver quality digital video services.
Although studies have been done in blur identification, most blur identification approaches, such as spectrum, bispectrum and maximum likelihood based approaches, were developed under the assumption of uniform blur or under the restriction of only tolerating the existence of white Gaussian noise. The blurring effect resulting from block Discrete Cosine Transform (DCT) based compression may vary from one block to another due to different quantization parameters used for coding different macroblocks. Additionally noise originated from quantization of DCT components is not independent in the spatial domain, but contributes to blocking errors and high frequency noise around edges. Blur distortion in MPEG and H.263 video sequences is caused by DCT quantization. When the quantization truncates the high frequency DCT coefficients completely, the loss appears as blur in the video sequence. Because quantization level changes across block boundaries, the resulting blur as well as quantization noise vary accordingly.
A single-ended blur estimation scheme was proposed by Jiuhuai Lu in the Proceedings SPIE 4301—Machine Vision Applications in Industrial Inspection—San Jose, Calif., January 2001 in a paper entitled “Image Analysis for Video Artifact Estimation and Measurement”. The scheme has several steps, including pre-processing to eliminate artifacts that produce spurious edges, evaluation point selection to choose appropriate places for blur estimation, blur estimating at each selected evaluation point and averaging to provide a frame-based blur estimate. For blur estimation edges of blocking boundaries may be reduced by simple lowpass filtering. The evaluation point selection determines a set of image edge points on the basis of edge intensity and connectivity to eliminate blurring due to other than quantization in compression. Finally a statistical approach is used to estimate the extent of picture blur by extracting an edge profile spread at the selected edge points using a norm of the edge gradient as follows:                1) compute the gradient vector at the current evaluation point,        2) sample the edge image e(x,y) along both sides of the gradient vector centered at the evaluation point,        3) set the data series of the sampled edge values as the edge profile at the evaluation point,        4) compute the edge profile spread.The edge profile p is centered at point (x,y) being evaluated and is equally spaced. An autocorrelation of the edge profile is obtained and used to obtain the edge profile spread. The edge profile is corrected if it has a tilted baseline before computing the autocorrelation function. The blur estimation of the video picture or frame is the average of the edge profile spread at all chosen evaluation points. This disclosed technique is general and does not provide specific details for providing an accurate and robust measurement, and only applies to single-ended applications, not to double-ended or reduced-reference applications.        
What is desired is to provide an accurate, robust, repeatable and computationally feasible method of measuring blurring in video sequences for use in any application that requires video quality measurement.